Abstract
Cloud computing is a suitable platform for running applications to process large volumes of data. Currently, with the growth of geographic and spatial data volume, conceptualized as Big Geospatial Data, some tools have been developed to allow the processing of this data efficiently. This work presents a cost-efficient method for processing geospatial data, optimizing the number of data nodes in a SpatialHadoop cluster according to dataset size. With this, it is possible to analyse and compare the costs for this type of application on public cloud providers.
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Bachiega, J., Reis, M.S., Araújo, A.P.F., Holanda, M. (2018). Cost Analysis for Big Geospatial Data Processing in Public Cloud Providers. In: Ferguson, D., Muñoz, V., Cardoso, J., Helfert, M., Pahl, C. (eds) Cloud Computing and Service Science. CLOSER 2017. Communications in Computer and Information Science, vol 864. Springer, Cham. https://doi.org/10.1007/978-3-319-94959-8_12
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DOI: https://doi.org/10.1007/978-3-319-94959-8_12
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